Social and Infrastructural Conditioning of Lowering Energy Costs and Improving the Energy Efficiency of Buildings in the Context of the Local Energy Policy

The main problem in creating successful efficiency improvement policies is adjusting objectives to local development programs, dependent on public awareness. This article attempts to find a framework for the costs of changing energy policies using neural networks to identify the social-infrastructure conditions. An analysis model is presented of social-infrastructure conditions of energy costs reduction and buildings' efficiency improvement. Data were obtained from standardized interviews with Zielona Gora, Poland inhabitants and the Town Energy Audit documentation. The data were analyzed using an artificial neural network. This allowed the creation of a model to estimate the cost inhabitants will incur if the energy is sourced from RES (Renewable Energy Systems). The city social-infrastructural correlation model enabled diagnosing its fragments that can support decision-making. The paper contributes to the current knowledge demonstrating the possibilities of hierarchical investments, different for various buildings and neighborhoods, that allow for rational public funding. Knowledge of the correlation conditions matters when implementing effective local policy. This work is based on pilot studies not financed by the parties concerned. Multiple themes were intentionally investigated: emission control, reducing energy consumption, renovating buildings, supplying with RES, and energy poverty, to show methods to match the goal (hard) to social conditions (soft), rarely presented in studies.